A dual membership based fuzzy support vector machine algorithm

Authors:
Huang Ying;Li Kang-shun
Affiliations:
School of Mathematics and Computer, Gannan Normal University, Jiangxi, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Year:
2009

Citing 3
Cited 0

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Abstract

A novel dual membership based fuzzy support vector machine (DM-FSVM) is presented while traditional fuzzy support vector machine (FSVM) is anal sized. There is only one membership in the samples of training sets of traditional SVM model, but in DM-FSVM, there are two memberships. The theoretically and simulate experiments show that this new method not only can keep the advantages of traditional FSVM, but also makes fully use of limited data and improves the classification efficiencies and the classification accuracy.